Differential genetic mutations and immune cell infiltration in high‐ and low‐risk STAD: Implications for prognosis and immunotherapy efficacy

Abstract This study investigates genetic mutations and immune cell dynamics in stomach adenocarcinoma (STAD), focusing on identifying prognostic markers and therapeutic targets. Analysis of TCGA‐STAD samples revealed C > A as the most common single nucleotide variant (SNV) in both high and low‐risk groups. Key mutated driver genes included TTN, TP53 and MUC16, with frame‐shift mutations more prevalent in the low‐risk group and missense mutations in the high‐risk group. Interaction analysis of hub genes such as C1QA and CD68 showed significant correlations, impacting immune cell infiltration patterns. Using ssGSEA, we found higher immune cell infiltration (B cells, CD4+ T cells, CD8+ T cells, DC cells, NK cells) in the high‐risk group, correlated with increased risk scores. xCell algorithm results indicated distinct immune infiltration levels between the groups. The study's risk scoring model proved effective in prognosis prediction and immunotherapy efficacy assessment. Key molecules like CD28, CD27 and SLAMF7 correlated significantly with risk scores, suggesting potential targets for high‐risk STAD patients. Drug sensitivity analysis showed a negative correlation between risk scores and sensitivity to certain treatments, indicating potential therapeutic options for high‐risk STAD patients. We also validated the carcinogenic role of RPL14 in gastric cancer through phenotypic experiments, demonstrating its influence on cancer cell proliferation, invasion and migration. Overall, this research provides crucial insights into the genetic and immune aspects of STAD, highlighting the importance of a risk scoring model for personalized treatment strategies and clinical decision‐making in gastric cancer management.


| INTRODUC TI ON
5][6] The complexity of gastric cancer arises not only from its biological heterogeneity but also from its intricate interactions with the tumour microenvironment (TME). 7,8[11] Among the diverse components of the TME, the immune microenvironment has become a focal point of research interest. 12,137][18] The delicate balance between immune activation and suppression, orchestrated by these cells, profoundly influences clinical outcomes and therapeutic responsiveness.
0][21] However, not all gastric cancer patients respond uniformly to immunotherapeutic interventions. 22,23Stratifying patients based on their likelihood of benefiting from immunotherapy has become crucial to personalize treatments and enhance clinical outcomes. 24other significant challenge in gastric cancer treatment is drug resistance. 25,26Despite the availability of various therapeutic agents, many patients face the threat of relapse due to the emergence of drug-resistant tumour clones. 27Deciphering the molecular mechanisms underlying drug resistance could pave the way for more effective and tailored treatment regimens.
In light of these challenges, this study was conceived with the overarching objective of exploring genetic alterations and variations in the immune landscape of gastric cancer.The aim is to uncover potential biomarkers and therapeutic targets by analysing mutational patterns, immune cell infiltration and hub gene interactions.
Through this approach, we seek to unravel the intricacies of the TME in gastric cancer and provide a detailed understanding of its implications for patient prognosis and drug sensitivity.

| Data acquisition
Gene expression data and extensive clinical annotations were retrieved from the GEO public database for this investigation.The study integrated bulk RNA-seq data from 320 stomach adenocarcinoma patients in the TCGA-STAD cohort, along with bulk RNAseq data from an additional 433 stomach adenocarcinoma patients sourced from GSE84437.Additionally, single-cell RNA-seq (scRNAseq) data from five stomach adenocarcinoma patients within GSE167297 were included for further analyses.

| Single-cell sequencing analysis
Initially, scRNA-seq data from the five stomach adenocarcinoma samples in GSE167297 underwent single-cell analysis, which led to the

| Visualization
The single-cell RNA-seq (scRNA-seq) data underwent rigorous quality control measures prior to analysis.Cells containing more than 25% mitochondria-associated genes were systematically excluded to ensure data integrity.Subsequently, the top 2000 highly variable genes from each sample underwent normalization using the ScaleData function, which is based on the variance stabilization transformation (vst) method.
To reduce dimensionality and capture essential features, the RunPCA function was applied with a dimensionality setting of 20.
Following this, cells were classified into distinct groups through the utilization of the 'FindNeighbors' and 'FindClusters' functions.A resolution parameter of 0.5 was chosen to optimize the clustering process and enhance the delineation of cell populations.
Uniform manifold approximation and projection (UMAP), a nonlinear dimension reduction technique within the Seurat package, were employed to transform high-dimensional cellular data into a two-dimensional space.This facilitated the grouping of cells with similar expression patterns while effectively segregating those with divergent patterns.The implementation of UMAP aimed to provide a clear and informative representation of the cellular heterogeneity present in the scRNA-seq data.

| Cell-cell interaction analysis
Drawing on ligand-receptor intel, the single-cell gene expression matrix was used to decipher communication among immune cell | 3 of 14 subtypes.This was achieved with the CellChat software (http:// www.cellc hat.org/ ) using default settings.The software modelled the probability of communication and pinpointed significant communications.

| Differential analysis
Using the FindAllMarkers function from the Seurat package, we compared the Panoptosis-High and Panoptosis-Low groups, selecting differentially expressed genes (DEGs) with an adjusted p-value < 0.05 and an absolute logFC > 0.585.These genes were significantly enriched in functions and pathways.

| Functional enrichment analysis
Differentially expressed genes between the Panoptosis-High and Panoptosis-Low groups underwent GO and KEGG analysis.The analysis was executed using the R package 'clusterProfiler' (version 4.0.5).A false discovery rate (FDR) of <0.05 was set as the criterion for significant enrichment.

| Prognostic risk model
COX survival analysis on the DEGs was conducted using the tinyarray package with a p-value < 0.05, identifying 145 genes.In recent years, an increasing number of studies have adopted machinelearning techniques to screen and select core genes for various medical and biological applications. 28,29A Lasso regression selected 5 prognostically significant genes from the 17 candidates, forming a prognostic model.The selected genes from the GSE84437 cohort were C1QA, MARCKSL1, RPL14, N4BP2L2, and CD68.The Risk score was created based on the median risk, distinguishing between the low-risk and high-risk groups.The Risk score equation was as follows: C1QA0.310 − MARCKSL10.203+ RPL14 × 0.575 − N4BP2 L2 × (−0.430) + CD68 × 0.113.The training set exhibited significant prognostic differences between the two groups.This was externally validated using the 433 stomach adenocarcinoma patients from the GSE84437 cohort, showing pronounced prognostic discrepancies between the two groups.

| Immune infiltration analysis
Using the 'GSVA' R package, we compared the enrichment and relative abundance of 23 tumour-infiltrating immune cell types within individual samples.Using the 'xCell' R package, machine-learning algorithms extracted the signature of 64 immune and stromal cells.Box plots, heatmaps and scatter plots were employed for visualization.

| Drug sensitivity analysis
Drug sensitivity was calculated using one method: Sensitivity scores for drugs in the GDSC database were computed using the 'oncoPredict' R package.

| Cell culture and transfection
In Transfection experiments were conducted with both cell lines.
Commercially designed siRNA (Sangon, China) was employed to knock down the expression of RPL14, with si-negative control used as a reference.Cells were detached from culture flasks using trypsin (KeyGEN, China), washed with PBS and centrifuged twice before resuspension in culture medium.Cell concentrations were calculated, and they were uniformly seeded in 6-well plates at a density of 2 × 10 4 cells per well.After cells adhered to the wells, a mixture of siRNA and LipofectamineTM 3000 (Invitrogen, USA) was prepared, allowed to stand at room temperature for 15 min, and then gently centrifuged for 1 min.The mixture was evenly dispensed into the wells using a pipette.After 4 h of transfection, the culture medium was replaced, and subsequent experiments were conducted 48 h post-transfection.

| CCK-8 assay
Following a 48-h transfection period, cells were digested with trypsin (KeyGEN, China), and the resulting cell suspension was evenly distributed in complete culture medium.Based on cell counts, the cells were transferred to 96-well plates (5000 cells per well).To ensure result accuracy, three replicate wells were set for each group.
Following cell adhesion as observed under a microscope, CCK-8 reagent (KeyGEN, China) was mixed with complete culture medium at a volume of 200 μL per well, following the manufacturer's instructions.The plate was then wrapped in aluminium foil to protect from light, and the absorbance at 450 nm was measured on an instrument after 1.5 h.This process was repeated at 24, 48, 72 and 96-h time points.

| EDU assay
Cells transfected for 48 h were cultured in 96-well plates, with each well seeded with 1 × 10 4 cells.After cell attachment, 100 μM 2× EdU was added to each well and incubated for 2 h.Subsequently, cells were washed with PBS, fixed with 100 μL of universal cell fixative for 15 min, washed again and treated with 0.3% Triton X for 15 min.
Following additional washes, cells were stained with a fluorescent dye for 30 min.Finally, Hoechst 33342 was added to each well for nuclear staining.This experiment was repeated three times to ensure result reliability.

| Transwell assay
Matrigel (Corning, USA) was diluted at a 1:7 ratio, and 45 μL was added to each Transwell chamber (Corning, USA).Chambers were placed on a clean bench for 36 h to ensure sterility.Subsequently, 700 μL of complete culture medium was added to the wells of a 24-well plate.After 48 h of transfection, cells were digested with trypsin and suspended in FBS-free culture medium.Cells were counted, and 20,000 cells were added to each chamber, with culture medium added to reach a total liquid volume of 170 μL.
Chambers were then placed in the 24-well plate, submerged in the liquid surface of complete culture medium and incubated for 24 h.
Following incubation, liquid was removed, and chambers were gently washed with PBS.Cells were fixed with paraformaldehyde for 30 min, washed, and non-invading cells were wiped off with a moist cotton swab.Cells were then stained with 0.1% crystal violet solution for 20 min, washed three times and photographed under a microscope.This experiment was repeated to ensure result reliability.

| Wound healing assay
After 48 h of transfection, a 200 μL pipette tip was used, with the assistance of a ruler, to draw a vertical line in each well.The pipette tip was changed before each scratch to prevent cross-contamination.
After making the scratches, the culture medium was removed, and wells were gently washed twice with PBS to remove floating cells.
Subsequently, 2 mL of serum-free base culture medium was added to each well.This point was defined as the 0-h time, and wound area was recorded.After 24 h of incubation in the cell culture incubator, photographs were taken, and wound closure area was recorded to calculate wound healing percentages.

| Statistical analysis
All statistical analyses were performed using the R language.Cox regression analyses were executed using the survival and survminer R packages for both univariate and multivariate Cox regression, with a p-value < 0.05 set as the threshold for prognostic significance.

| Delineation between panoptosis-high and panoptosis-low and identification of DEGs
We

| Establishment and validation of prognostic risk model
For prognostic purposes, a total of 145 genes were initially identified using COX survival analysis.Lasso regression then narrowed these down to five key prognostic genes: C1QA, MARCKSL1, RPL14, a positive correlation.Furthermore, the expression levels of these genes between the risk groups were distinctively displayed in the box plots of Figure 4E.To validate the prognostic potential and assess the efficacy of immunotherapy, we examined the correlation between the risk score and the expression of immunotherapy-related markers.Figure 4F demonstrates a significant positive correlation between the risk score and the expression levels of markers such as CD28, CD27, SLAMF7, TIGIT, ICOS and CCL5.Notably, these molecules exhibit significantly elevated expression levels in the high-risk group, suggesting that patients in the high-risk category may derive substantial benefits from immunotherapy.

| Analysis of genetic mutations between high and low-risk groups
In the analysis of genomic variations, Figure 5A  emerged as the top three driver genes with the highest mutation frequency across these groups.The analysis also revealed a distinct pattern in the types of mutations prevalent in each risk group: frameshift mutations were most common in the low-risk group, whereas missense mutations predominated in the high-risk group.Further exploration into the genetic interplay was conducted through an analysis of the interaction among five hub genes.Figure 5C illustrates significant correlations between these genes, where a notable positive correlation was observed between C1QA and CD68, and a significant negative correlation between MARCKSL1 and N4BP2L2.
These correlations provide insights into the potential synergistic or antagonistic roles these genes may play in the pathogenesis of the disease.

| Differential immune infiltration analysis between high and low-risk groups
In their comprehensive investigation, we focused on the differential infiltration of immune cells within high and low-risk groups of STAD to understand the complex interplay within the tumour immune microenvironment and its impact on survival outcomes.
Utilizing the ssGSEA algorithm, Figure 6A displays the contrasting profiles of immune cell infiltration between these two risk groups.

| Drug sensitivity analysis
In their investigation of the relationship between clinical drug resistance and risk scores in stomach adenocarcinoma, we performed a comprehensive drug sensitivity analysis.This analysis, as illustrated in Figure 8A, revealed a significant positive correlation between the expression of C1QA and the sensitivity to several drugs, including Tozasertib, RO3306 and Axitinib.This finding suggests that higher

| The oncogenic role of RPL14 in gastric cancer progression
In a focused investigation of RPL14's influence on gastric cancer, we utilized siRNA to downregulate its expression in two distinct gastric cancer cell lines.This approach enabled us to conduct a series of phenotypic assays, each offering insights into RPL14's role in cancer progression.The CCK-8 assay findings were striking, displaying a substantial reduction in cell absorbance upon Additionally, wound healing assay results provided further evidence of RPL14's impact, with a notable decline in scratch closure capability in cells with diminished RPL14 expression, indicating its contribution to gastric cancer cell migration (p < 0.001, Figure 9D).
Taken together, these results cohesively suggest that RPL14 plays a critical role in gastric cancer pathogenesis, significantly contributing to the proliferation, invasion and migration of gastric cancer cells.This study not only highlights the oncogenic properties of RPL14 but also underscores its potential as a target for therapeutic intervention in gastric cancer treatment.

| DISCUSS ION
Our study presents a comprehensive analysis of genetic mutation patterns and immune cell infiltration in STAD, offering insights into the potential prognostic indicators and therapeutic targets for both high and low-risk groups.This discussion aims to contextualize our findings within the broader landscape of current research, compare them with other studies and explore their implications for future therapeutic strategies.
Our observation that C > A SNVs are predominant in both high and low-risk STAD groups is significant.Previous studies have similarly identified C > A transversions as common in various cancers, including lung and oesophageal carcinomas, often associated with exposure to specific mutagens like tobacco smoke. 30However, the prevalence of these SNVs in STAD, as revealed in our study, underscores a potentially unique mutational signature that might be intrinsic to gastric carcinogenesis.
Mutations in TTN, TP53 and MUC16 were identified as top drivers in our study.The TP53 gene, often dubbed the 'guardian of the genome', is well-known for its mutation in various cancers, acting as a pivotal point in tumorigenesis. 31The mutation of TP53 in highrisk STAD groups aligns with these findings, suggesting a common pathway in oncogenesis.However, the roles of TTN and MUC16 mutations are less explored in STAD.TTN mutations have been implicated in cardiac diseases but their role in cancer, especially in STAD, opens new avenues for research. 32terestingly, frame-shift mutations were predominantly observed in the low-risk group, while missense mutations were more common in the high-risk group.This contrasts with findings in colorectal cancer, where frame-shift mutations in the APC gene are linked to higher malignancy. 33Our results suggest a distinct mutational mechanism in STAD, where the type of mutation could influence tumour behaviour and patient prognosis.
The positive correlation between C1QA and CD68 and the negative relationship between MARCKSL1 and N4BP2L2 are novel findings in STAD research.In lung cancer, CD68 has been associated with tumour-associated macrophages and linked to poor prognosis, 34 but its relationship with C1QA in STAD suggests a unique immune modulation.Comparatively, the negative interaction of MARCKSL1 and N4BP2L2 is a relatively unexplored area and could be pivotal in understanding the immune evasion mechanisms in STAD.
Our use of the ssGSEA algorithm revealed higher infiltration of B cells, CD4 + T cells, CD8 + T cells, DC cells and NK cells in the highrisk group.This finding aligns with studies that have demonstrated the role of these immune cells in tumour suppression and progression in various cancers. 35The differential immune landscape in high and low-risk groups highlights the complex interplay between the tumour microenvironment and immune response in STAD.
The correlation of risk scores with immune cell infiltration levels and the identification of key molecules such as CD28, CD27 and SLAMF7 suggest potential therapeutic targets.These molecules have been studied in the context of immune checkpoint therapies in other cancers. 36Our findings suggest their relevance in STAD, especially in stratifying patients for immunotherapy.
Our research uncovers novel insights into personalized treatment options for high-risk STAD patients, particularly through the negative correlation between risk scores and drug sensitivity.A pivotal aspect of our study is the establishment of RPL14 as a carcinogenic factor in gastric cancer, corroborating existing literature on the role of ribosomal proteins in cancer. 37This discovery has profound implications for both research and clinical which are integral to cell proliferation and survival.Additionally, changes in gene expression related to cell cycle and apoptosis suggest RPL14's influence on these processes.Our Transwell invasion and wound healing assays further revealed that reduced RPL14 expression leads to decreased invasion and migration capacities in gastric cancer cells.We hypothesize that RPL14's effects on EMT processes MMPs activity are significant in this regard.These findings not only confirm RPL14's role in gastric cancer aggressiveness but also its potential as a therapeutic target.In summary, RPL14's impact on key behaviours like proliferation, invasion and migration highlights its critical role in gastric cancer pathogenesis.
Our findings advocate for further exploration into RPL14's molecular mechanisms and interactions within the tumour microenvironment, focusing on the pathways it influences.
While our study provides valuable insights, there are limitations.
The retrospective nature of the study and reliance on existing da-

identification of 7
distinct cell clusters.The AddModuleScore function in the Seurat package was employed to compute the enrichment score for the Panoptosis pathway, yielding a Panoptosis score.Each immune cell cluster was then categorized into Panoptosis-High and Panoptosis-Low groups.GSVA scores for 50 cancer-related Hallmark pathways were calculated for the seven cell sub-clusters, illustrating the distinctions among them.Additionally, Cellchat was computed to depict intercellular communication across the different cell clusters.

3 | RE SULTS 3 . 1 |
High-resolution scRNA-seq deciphers the immune landscape of STADMarker gene analysis provided deeper insights into each cell type's identity.For instance, T cells were characterized by markers such as PTPRC, CD3E and CD3D; B cells were identified mainly by CD79A expression; myeloid cells were distinguished by markers like S100A9 and LYZ; epithelial cells were marked by KRT19; endothelial cells were identified by RAMP2 expression; and adipocytes were characterized by TPSAB1 (Figure1A).Thereafter, we successfully classified the cells into seven distinct clusters: T cells, B cells, fibroblasts, endothelial cells, myeloid cells, epithelial cells and adipocytes, as demonstrated in the UMAP visualization (Figure1B).This plot revealed the unique cellular composition and distribution across the different patients.Notably, the analysis highlighted a predominant presence of B cells in patient 5, while T cells were primarily observed in patients 4 and 5 (Figure1C).The stacked bar graph (Figure1D) further elucidated the predominance of T cells and B cells in the patient cohort.Moreover, to explore the dynamics of tumour modulation, the study delved into the interactions between immune cells during tumorigenesis.A Cellchat diagram (Figure2A) provided insights into the interaction strengths among these cells.It revealed strong interactions between T cells, B cells and myeloid cells.Interestingly, adipocytes were found to have negligible strong interactions with other cell types, suggesting a more isolated role in the tumour microenvironment.

| 5 of 14 DENG
further explored the Panoptosis pathway and developed a Panoptosis score for different cell subtypes, as shown in Figure 2B.This analysis revealed that myeloid cells exhibited significantly higher Panoptosis scores.Supporting this finding, the single-cell clustering in Figure 2C demonstrated a cluster with high Panoptosis scores, predominantly consisting of mononuclear cells.In the study, the GSVA enrichment analysis depicted in Figure 2D highlighted key pathways predominantly enriched by various cell subtypes.Specifically, T cells were shown to play a crucial role in several pathways, including HEDGEHOG-SIGNALLING, EPITHELIAL MESENCHYMAL TRANSITION, ANGIOGENESIS and NOTCH SIGNALLING.Further investigation into the Panoptosis-High and Panoptosis-Low groups led to the selection of DEGs, which were then subjected to GO and KEGG enrichment analysis.The GO analysis in Figure 3A indicated that these DEGs were primarily enriched in functions related to et al.Cytoplasmic translation, Ribosome and Structural constituent.The KEGG pathway analysis, as shown in Figure 3B, associated these DEGs with coronavirus diseases COVID 19 and Ribosome.

F I G U R E 1
N4BP2L2 and CD68, as evidenced in Figure 3C,D.Utilizing a specific risk score formula, patients were classified into low-risk and highrisk groups.Kaplan-Meier curves in Figure 3E revealed a significant survival difference between these two groups within the training set.This was further corroborated by scatter plots in Figures 3F,G, which showed a predominant survival trend in the low-risk group, in contrast to the high-risk group.The validation of these findings in a separate dataset (Figures 4A-C) mirrored the training set results.Multivariate COX regression analysis, presented in Figure 4D, indicated that among the identified genes, C1QA and RPL14 were inversely associated with STAD prognosis, while N4BP2L2 exhibited Cellular landscape in stomach adenocarcinoma.(A) Dot plot illustrating specific marker genes for various cell populations.(B) UMAP visualization depicting the clustering of seven cell populations after dimensionality reduction.(C) UMAP plot showing the cell composition percentages in five stomach adenocarcinoma patients.(D) Stacked bar graph representing the proportion of different cell populations in five stomach adenocarcinoma patients.

F I G U R E 2 | 7 of 14 DENG
highlighted that the C > A SNV was predominantly observed across all TCGA-STAD Cellular communication and functional analysis in cancer cells.(A) Cellchat diagram of different cell populations.(B) Box plot of cell subpopulations based on the Panoptosis score.(C) UMAP plot contrasting cells with high and low scores in the pan-apoptosis gene set.(D) Heatmap depicting the GSVA scores of different cell populations based on hallmark pathways.et al. samples within both high and low-risk groups.This pattern underscores the prominence of this particular SNV in stomach adenocarcinoma.The mutation landscape of the top 20 driver genes, as depicted in Figure 5B's waterfall plot, provides a detailed account of the mutation distribution and the proportion of mutated samples in the high and low-risk groups.Notably, TTN, TP53 and MUC16

F I G U R E 3
Gene expression and survival analysis in stomach adenocarcinoma.(A) Dot plot of GO analysis for DEGs.(B) Bar-dot plot showing the KEGG pathway analysis of DEGs.(C, D) Lasso regression analysis.(E-G) KM survival curves for the training dataset.Notably, the high-risk group exhibited significantly elevated levels of B cells, CD4 + T cells, CD8 + T cells, DC cells and NK cells compared to the low-risk group, highlighting a distinct immune profile associated with higher risk (p < 0.05).Moreover, the correlation matrix in Figure 6B underscores substantial positive associations among 23 types of immune cell infiltration levels, as exemplified by a significant positive correlation between immature and activated B cells.Furthering this analysis, Figure 6C presents a significant positive correlation between the risk score and various immune cells.This includes B cells, CD4 + T cells, CD8 + T cells, DC cells, NK cells, NKT cells, mast cells and macrophages (p < 0.05), suggesting a heightened presence of these immune cells in the high-risk group.In exploring the links between the five key hub genes used in the modelling and the abundance of immune cell infiltration, Figure 7A reveals significant correlations.For instance, there is a positive correlation between RPL14 and CD8 T cells, N4BP2L2 with B cells and Type 2 Th cells, MARCKSL1 with CD8 T cells, CD68 with eosinophils and C1QA with Type 17 Th cells.Employing the xCell algorithm for a deeper dive into immune infiltration, Figure 7B,C shows that the low-risk group, in contrast to the high-risk group, exhibits significantly increased infiltration levels of DC cells, B cells, memory CD4 + T cells, naive CD4 + T cells, M2 macrophages, MSC, endothelial cells, among others (p < 0.05).

F I G U R E 4
Risk assessment and immune checkpoint analysis in cancer patients.(A-C) KM survival curves for the validation dataset.(D) Multivariate Cox proportional hazards forest plot for hub genes in TCGA-STAD.(E) Box plots comparing the expression levels of key genes in high-risk and low-risk groups.(F) Comparison of immune checkpoint inhibitor target gene expression between high-risk and low-risk groups.DENG et al.C1QA expression may be indicative of an increased response to these specific drugs.Additionally, Figure 8B presents a significant negative correlation between the calculated risk score and the effectiveness of certain drugs, specifically XAV939-1268, CDK9-5038-1709 and Niraparib-1177.This implies that patients categorized within the high-risk group are likely to be more responsive to these drugs.Such insights are crucial in guiding personalized treatment plans, as they indicate potential therapeutic effectiveness based on individual tient risk profiles.

F I G U R E 5
Figure 9B).Invasion capabilities, assessed via Transwell assays, revealed a marked reduction in the invasive potential of cancer cells following RPL14 knockdown, pointing to its pivotal role in facilitating gastric cancer cell invasion (p < 0.001, Figure9C).

F I G U R E 6 | 11 of 14 DENG
Immune infiltration analysis in cancer risk groups.(A) Box plot depicting the immune infiltration analysis of high-risk and lowrisk groups based on the ssGSEA algorithm.(B) Correlation plot of 23 immune infiltration cell types.(C) Scatter plot showing the correlation between hub genes and various immune cells in the immune microenvironment, based on ssGSEA.et al.

F I G U R E 7
Immune cell abundance and correlation with key genes.(A) Dot plot illustrating the correlation between the abundance of immune cell infiltration and five key genes used in model building.(B) Box plot comparing the immune infiltration analysis of high-risk and low-risk groups using the xCell algorithm.(C) Heatmap displaying the correlation between five key genes and 67 immune infiltration cell types assessed by xCell.

F I G U R E 8
practice.Our analysis of varying mutational patterns and immune profiles in different STAD risk groups underscores the potential of personalized therapies, especially targeted immunotherapies and risk-based treatment protocols, in improving patient outcomes.Experimentally, we've demonstrated the oncogenic role of RPL14 in gastric cancer.Assays like CCK-8 and EDU incorporation indicate that RPL14 knockdown significantly reduces cell proliferation, emphasizing its role in gastric cancer cell growth.We delved deeper into the mechanisms of RPL14, finding that it may modulate critical pathways like PI3K/Akt and MAPK/ERK, Drug sensitivity and genetic correlation in cancer therapy.(A) Heatmap showing the correlation between eight key genes used in model building and drug sensitivity to various related drugs.(B) Dot plot representing the correlation of risk score with the sensitivity to different drugs.
tabases like TCGA limit the scope for real-time PCR analysis and experimental validation.Future studies should focus on prospective data collection and the integration of genomic with clinical data to better understand the biological mechanisms underlying our observations.In summary, our study sheds light on the genetic and immune landscapes of STAD, highlighting significant differences between high and low-risk groups.By comparing our findings with existing literature, we underscore the uniqueness of STAD's mutational and immune profiles.These insights pave the way for more personalized and effective treatment strategies, emphasizing the importance of continued research in this field.Conceptualization (equal); data curation (equal).Si-jia Wu: Conceptualization (equal).Tian-ying Zhang: Conceptualization (equal).Jie Yang: Conceptualization (equal).Kai Liu: Conceptualization (supporting).ACK N OWLED G EM ENTSNone declared.

F I G U R E 9
The oncogenic role of RPL14 in gastric cancer cell proliferation, invasion and migration.(A) CCK-8 assay results demonstrating decreased cell proliferation in gastric cancer cells upon RPL14 knockdown.A significant reduction in absorbance is observed in cells with suppressed RPL14 expression compared to the control group (p < 0.001).(B) EDU incorporation assay outcomes highlighting the impact of RPL14 on cancer cell proliferation.The results show a notable decrease in fluorescent intensity in cells with RPL14 knockdown, compared to control cells, indicating reduced proliferation (p < 0.001).(C) Transwell invasion assay findings depicting a marked reduction in the invasive capacity of gastric cancer cells following RPL14 suppression.The lower number of invading cells in the RPL14 knockdown group, as compared to controls, signifies its role in promoting invasion (p < 0.001).(D) Wound healing assay results revealing the influence of RPL14 on cell migration.A significant decrease in the rate of scratch closure is evident in the RPL14 knockdown group compared to the control group, suggesting a promotive effect of RPL14 on gastric cancer cell migration (p < 0.001).